On Scheduling Fees to Prevent Merging, Splitting and Transferring of Jobs

A deterministic server is shared by users with identical linear waiting costs, requesting jobs of arbitrary lengths. Shortest jobs are served first for efficiency. The server can monitor the length of a job, but not the identity of its user, thus merging, splitting or partially transferring jobs offer cooperative strategic opportunities. Can we design cash transfers to neutralize such manipulations? We prove that merge-proofness and split-proofness are not compatible, and that it is similarly impossible to prevent all transfers of jobs involving three agents or more. On the other hand, robustness against pair-wise transfers is feasible, and essentially characterize a one-dimensional set of scheduling methods. This line is borne by two outstanding methods, the merge-proof S+ and the split-proof S?. Splitproofness, unlike Mergeproofness, is not compatible with several simple tests of equity. Thus the two properties are far from equally demanding.

Induced Rules for Minimum Cost Spanning Tree Problems:towards Merge-proofness and Coalitional Stability

This paper examines cost allocation rules for minimum cost spanning tree (MCST) problems, focusing on the properties of merge-proofness and coalitional stability. Merge-proofness ensures that no coalition of agents has the incentive to merge before participating in the cost allocation process. On the other hand, coalitional stability ensures that no coalition has the incentive to withdraw from the cost allocation process after the cost allocation proposal is made. We propose a novel class of rules called induced rules, which are derived recursively from base rules designed for two-person MCST problems. We demonstrate that induced rules exhibit both merge-proofness and coalitional stability within a restricted domain, provided that the corresponding base rules satisfy specific conditions

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

A Pay-as-Bid Mechanism for Pricing Utility Computing

Encountering the increasing demand for high-performance computational resources in academic as well as commercial organisations, utility computing offers a solution by providing users with on-demand availability of requested computing services. Approaches to the fundamental issue of resource allocation include the use of technical scheduling mechanisms as well as introducing economic ideas into the allocation schemes. Technical scheduling mechanisms are often very simple (such as first-in-first-out) but suffer under the shortcoming to adequately prioritize jobs in times when demand exceeds supply. As empirical studies show, Grids (such as PlanetLab) are frequently characterized by huge excess demand for resources. This is where economic models such as markets come into play. Hitherto, market mechanisms are either (too) simple or too complex for usage in Grids. The contribution of this paper is threefold. Firstly, a mechanism for Grids is proposed, which is still simple but geared up for use in the Grid. Secondly the mechanism is embedded in state-of-the-art Grid middleware Sun N1 Grid Engine 6. Thirdly, it is shown by means of a numerical case study that this mechanism is superior to other commonly used mechanisms

Non-manipulable rules for land rental problems

We consider land rental problems where there are several communities that can act as lessors and a single tenant who does not necessary need all the available land. A rule should determine which communities become lessors, how much land they rent and at which price. Our first result is a complete characterization of the family of rules that satisfy land monotonicity and non-manipulability under land reassignment. We also define two parametric subfamilies. The first one is characterized by adding a property of weighted standard for two-person. The second one is characterized by adding consistency and continuity

Proportional scheduling, split-proofness and merge-proofness

If shortest (respectively longest) jobs are served first, splitting a job into smaller jobs (respectively merging several jobs) can reduce the actual wait. Any deterministic protocol is vulnerable to strategic splitting and/or merging. This is not true if scheduling is random, and users care only about expected wait. The Proportional rule draws the job served last with probabilities proportional to size, then repeats among the remaining jobs. It is immune to splitting and merging. Among split-proof protocols constructed in this recursive way, it is characterized by either one of three properties: job sizes and delays are co-monotonic; total delay is at most twice optimal delay; the worst (expected) delay of any job is at most twice the smallest feasible worst delay. A similar result holds within the family of separable rules